Question Answering: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.
Summarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
Querying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.
Evaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
Generate similar examples: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.
Compare models: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
Reference Docs#
All of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
Reference Documentation
LangChain Ecosystem#
Guides for how other companies/products can be used with LangChain
LangChain Ecosystem
Additional Resources#
Additional collection of resources we think may be useful as you develop your application!
LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.
Glossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!
Gallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.